Dynamics of sensorimotor adaptation
description
Transcript of Dynamics of sensorimotor adaptation
Dynamics of sensorimotor adaptation
Sen Cheng, Philip N SabesUniversity of California, San Francisco
Annual Swartz-Sloan Centers Meeting, 26th July 2005
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A simple sensorimotor task
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Motivation and outline
trial-by-trial dynamics
What is the learning rule of adaptation?1. What signals drive
learning?
2. Noise in the learning process?
3. Spatial anisotropies?
More powerful correlation between behavior and neural activity.
Steady-state of adaptation Compare average behavior
pre- and post-exposure
block design
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Virtual reality setup
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Concurrent test and exposure
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Model for dynamics of adaptation
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Linear dynamical system (LDS) ut : inputs (?)
xt : internal state, planned/expected reach error
yt : actual reach error
qt : learning noise
rt : motor noise
general state space model
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1. What signals drive learning?
2. Noise in the learning process?
3. Spatial anisotropies?
Questions
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Two candidate learning signals
System identification with expectation-maximization (EM) algorithm, Cheng and Sabes, 2005, submitted
Learning equation with two input signals
t : visual error
t : perturbation/ discrepancy betw. vision and proprioception
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Sample data and vis-model fit
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perturbation
reach error
model prediction
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Portmanteau test for serial autocorrelations
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Portmanteau statistic (Hosking, 1980)
Residual autocorrelations Portmanteau test for vis-model
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pert-model fit to sample data
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perturbation
reach error
vis-model
pert-model
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Portmanteau test cannot distinguish models
for vis-modelfor pert-model
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Likelihood ratio test (LRT) for nested models
M1: no input
M2: pert
M4: pert and vis
M3: vis error
p < 10-4 (n=18)p < 10-4 (n=18)
p=0.006 (n=1), p>0.067 (n=17) p>0.22 (n=18)
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ML
ML
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1. What signals drive learning?
2. Noise in the learning process?
3. Spatial anisotropies?
Questions
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The signal that drives learning
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apply to no feedback (noFB) reaches:
Estimated modelspert-model
vis-model
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pert-model
vis-model
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1. What signals drive learning? 2. Noise in the learning process?
3. Spatial anisotropies?
Questions
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Learning noise
stochastic
pert
LRT (n=18)
p < 10-4
noFB
LRT (n=18)p < 0.0003
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1. What signals drive learning? 2. Noise in the learning process? 3. Spatial anisotropies?
Questions
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Anisotropy in learning and noise
*
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Conclusions
LDS are good models for adaptation dynamics
New insights into adaptation1. Visual error drives adaptation predominantly
2. There is learning noise
3. Dynamics are anisotropic
Can now correlate trial-by-trial changes of behavior with neural activity.
supported by the Swartz foundation